BP neural network-based ABEP performance prediction for mobile Internet of Things communication systems
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A P P L Y I N G A R T I F I CI A L I N T E L L I G E N C E T O T H E I N T E R N E T O F T H I N G S
BP neural network-based ABEP performance prediction for mobile Internet of Things communication systems Lingwei Xu1 • Jingjing Wang1 • Han Wang2 • T. Aaron Gulliver3 • Khoa N. Le4 Received: 29 June 2019 / Accepted: 8 November 2019 Ó Springer-Verlag London Ltd., part of Springer Nature 2019
Abstract Wireless communications play an important role in the mobile Internet of Things (IoT). For practical mobile communication systems, N-Nakagami fading channels are a better characterization than N-Rayleigh and 2-Rayleigh fading channels. The average bit error probability (ABEP) is an important factor in the performance evaluation of mobile IoT systems. In this paper, cooperative communications is used to enhance the ABEP performance of mobile IoT systems using selection combining. To compute the ABEP, the signal-to-noise ratios (SNRs) of the direct link and end-to-end link are considered. The probability density function (PDF) of these SNRs is derived, and this is used to derive the cumulative distribution function, which is used to derive closed-form ABEP expressions. The theoretical results are confirmed by Monte-Carlo simulation. The impact of fading and other parameters on the ABEP performance is examined. These results can be used to evaluate the performance of complex environments such as mobile IoT and other communication systems. To support active complex event processing in mobile IoT, it is important to predict the ABEP performance. Thus, a back-propagation (BP) neural network-based ABEP performance prediction algorithm is proposed. We use the theoretical results to generate training data. We test the extreme learning machine (ELM), linear regression (LR), support vector machine (SVM), and BP neural network methods. Compared to LR, SVM, and ELM methods, the simulation results verify that our method can consistently achieve higher ABEP performance prediction results. Keywords Mobile Internet of Things Mobile cooperative communication Average bit error probability Performance prediction BP neural network
1 Introduction The rapid development of the mobile Internet of Things (IoT) has led to increased interest in mobile communication systems [1–5]. A novel mobile front haul architecture & Lingwei Xu [email protected] Jingjing Wang [email protected] 1
Department of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, China
2
College of Physical Science and Engineering, Yichun University, Yichun 336000, China
3
Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC V8W 2Y2, Canada
4
School of Computing, Engineering and Mathematics, Western Sydney University, Kingswood, Australia
was proposed in [6] for passive optical mobile networks. In [7], a two-dimensional anti-jamming mobile communication scheme was proposed which employs reinforcement learning techniques. As a promisin
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